Hugging Face Trending Papers October 5, 2026

Online AutoML: Evaluating Poisoning Attacks on Adversarial Training Defense Strategy in IoT Networks

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The paper investigates how poisoning attacks—specifically label flip and noise injection—affect online AutoML learners in IoT networks and evaluates the effectiveness of adversarial training (AT) as a defense. Experiments on streaming-capable models (Hoeffding Tree, Leveraging Bagging, Adaptive Random Forest, Hoeffding Adaptive Tree, and Streaming Random Patches) show that AT-SRP achieves the highest F1-score (0.904) against label flip attacks, while AT-LB achieves the highest F1-score (0.933) against noise injection. Drift detection methods were also employed for rolling accuracy and prequential evaluation.

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